Year: 2025
Type: article
Abstract: Skin cancer, a condition that originates in the skin tissue, can damage surrounding tissues and, in severe cases, lead to disability or even death. Early and accurate diagnosis, coupled with appropriate treatment, plays a crucial role in minimizing its harmful effects. However, diagnosing skin cancer can be challenging for physicians due to the visual similarities between cancerous lesions and benign tumors, often resulting in a time-consuming process. This project focuses on creating an automated system to distinguish between skin cancer and benign tumors using Convolutional Neural Networks (CNNs). The approach introduces a cutting-edge application of deep learning techniques, utilizing CNNs to analyze images of skin lesions uploaded for evaluation. In addition to providing accurate diagnostic support, the system delivers personalized recommendations.
Author: Prof. H. R. Khairnar
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409924686